4 Ways to Find the Most Valuable Policyholders

Advanced analytics are crucial to identifying customers with the highest lifetime value.

Who are your best customers? How can you make good customers better? Who should you try to lure away from your competition? Once you win them over, how can you secure their loyalty? Which customers are likely to defect, and how can you prevent them?

No two customers are the same, so why do insurance companies often ignore the customer when pricing their products? Research has shown that companies who understand customer value are 60% more profitable than those that do not. For example, life events such as marriage, buying a house or a new job will materially affect a customer's product needs, behavior and profitability. Acting on a present-day view of profitability, insurers could misread customers and provide the wrong kind of product or best advice.

Stuart Rose, SAS

To consider the real profit potential of customers, insurers are beginning to consider customer lifetime value when seeking to attract or retain customers. After all, not all customers are profitable ones.

Customer lifetime value (CLV) can be defined as the net present value of cash flow (past and future) attributed to a customer or household for a designated time period. Or, more simply, the difference between the total premium revenue received and total expenses over the course of the relationship. In many cases, this may be greater than 20 years.

CLV shows which customers will offer the highest value in the future, which can identify the core attributes insurers should look for in current customers and prospects.

Before beginning the path of customer lifetime value, it is essential to assemble the necessary historical data in analysis-ready format. While the information collected will be different for each insurer, typically it consists of data around customer demographics, customer experience, loyalty/tenure, premium revenue, cost to acquire and cost to maintain or serve.

Once insurers have created the data environment, the next step is to use analytics to build out the predictive models to project future customer value. This would include analysis for four key attributes:

The most conservative statistics say that it costs five times more to acquire a new customer than retain an existing customer. For the insurance industry, that figure easily jumps to 10 times more to generate new business. So customer retention is critical for insurers. Insurers need to mine the vast amount of customer and policy data available to predict which customers are likely to lapse and – most importantly – design cost-effective strategies to persuade them to remain a customer. Even simple indicators have shown to improve retention rates. For example, customers who pay in full have a higher retention rate compared with those on monthly payment plans.

Cross-sell and up-sell options

Cross-sell and up-sell is the ability to increase the wallet share of their customers. Cross-sell represents selling or bundling home insurance with auto insurance for the customer; up-sell represents adding additional coverage to a life policy. For both, insurers use analytical techniques such as market basket analysis to predict the next best product offer to the customer.

Power of influence

With the prevalence of online social networks and mobile connectivity, the idea of social influence is more important than in the past. Using analytics, insurance companies can now identify those connections and understand the spheres of influence, quantify them and use that data in the CLV calculations.

Customer lifetime value is still new for most industries, let alone insurance with its inverted business model. It is tempting to think that once you've calculated CLV and identified your highest-value customers, you then just focus on keeping them happy. A better approach is to learn from them, so you can improve the value of all your customers.

About the Author: Stuart Rose is global insurance marketing manager at Cary, N.C.-based SAS. Rose, a 20-year veteran of the insurance industry, began his career as an actuary. He has worked for a global insurance carrier in both its life and property divisions and has worked for several software vendors, where he was responsible for marketing, product management and application development. He has driven successful development and implementation of enterprise systems with insurance companies in the U.S., the U.K., South Africa and Continental Europe.

[Register for Interop here and check out the “Navigating the Big Spectrum of Big Data’s Solutions” session on October 4 in NYC.]